Quote of the week… “An ISP’s December usage was dramatically higher than the rest of the year. When they drilled down into the data, it was because blinking Christmas lights had caused video-stream compression ratios to drop dramatically.”

Apple WWDC 2018: 7 Things Around Machine Learning Announced This Year

This year at WWDC keynote, the iPhone maker made very low-key announcements on AI and ML, with no big surprise. This year they mainly focussed on software and improved on their existing features, in a way going back to its roots. But the fact remains that most of the features like Siri shortcuts, digital wellness, photos, group notification that were announced at the keynote have been there on Android devices for generations now. This year’s event was more like Apple playing a catch up with Google.

IBM Watson Gets Brawny Younger Brother in Summit Supercomputer

Looks like Watson has a new, younger, but somewhat brawnier, family member. On June 8 the U.S. Department of Energy’s Oak Ridge National Laboratory unveiled IBM’s Summit supercomputer and immediately billed it as the “world’s most powerful and smartest scientific supercomputer.”

As impressive as it certainly is, IBM and the DoE might get a legitimate argument about that lofty claim from other computers in the Top 500 around the world—namely in Guangzhou, China; Cern, Switzerland; and Japan. The Titan supercomputer in Oak Ridge, Tenn. was listed as No. 4 in the last ranking. We’ll have to see where Summit eventually ranks on the list, and it may well become No. 1.
2018-06-08 00:00:00 Read the full story.

CloudQuant thoughts... For the first time since 2013, and may fall back again. At least we know what this one does…

London claims to be AI capital of Europe

Not content with being Europe’s leading fintech hub, London is also staking a claim to the role of the EU’s Artificial Intelligence (AI) capital, home to double the number of AI companies than closest rivals Paris and Berlin combined The claim is made in a new piece of research commissioned by the Mayor of London, Sadiq Khan, that for the first time maps the capital’s AI ecosystem.

It found that London is home to 758 AI companies – double the total of Paris and Berlin combined. They specialise in more than 30 industries with particular strengths in insurance, finance and law.

CloudQuant Thoughts… The author of this article seems to have forgotten about Brexit. London may have a head start but it is still well behind China and the U.S.

Why Micron Is So Excited About Artificial Intelligence

Memory specialist Micron (NASDAQ:MU) sells both DRAM, a type of computer memory that’s used in virtually every kind of computing device, and NAND flash, which is rapidly gaining traction for high-performance data storage applications as it’s quicker and more efficient than hard disk drive-based storage.

Micron says that a typical machine learning training workload will require six times the amount of DRAM that a “standard cloud server” needs and twice as much solid state drive capacity. Those are pretty huge memory content increases.

In a graphic that Micron included in its analyst day presentation, it conceded that “AI-capable” servers made up just a small fraction of overall server shipments in calendar year 2017. However, it expects that percentage to grow substantially by calendar year 2021 and to get close to half of all server shipments by 2025.

CloudQuant Thoughts… Microns stock price has doubled in the last year. Have you been noticing the increased activity on this symbol and others in this industry. Time to write a sector/industry model on CloudQuant!

Why the Future of Machine Learning is Tiny

When Azeem asked me to give a talk at CogX, he asked me to focus on just a single point that I wanted the audience to take away. A few years ago my priority would have been convincing people that deep learning was a real revolution, not a fad, but there have been enough examples of shipping products that that question seems answered. I knew this was true before most people not because I’m any kind of prophet with deep insights, but because I’d had a chance to spend a lot of time running hands-on experiments with the technology myself. I could be confident of the value of deep learning because I had seen with my own eyes how effective it was across a whole range of applications, and knew that the only barrier to seeing it deployed more widely was how long it takes to get from research to deployment.

Instead I chose to speak about another trend that I am just as certain about, and will have just as much impact, but which isn’t nearly as well known. I’m convinced that machine learning can run on tiny, low-power chips, and that this combination will solve a massive number of problems we have no solutions for right now.

CloudQuant Thoughts… Do not know how entirely practical this is but the writer touches on a number of interesting points.

Going Dutch: How I Used Data Science and Machine Learning to Find an Apartment in Amsterdam — Part I

Amsterdam’s Real Estate Market is experiencing an incredible ressurgence, as someone making the move this posed like an interesting subject to me. In Amsterdam, property rental market is said to be as crazy as property purchasing market. I decided to take a closer look into the city’s rental market landscape, using some tools (Python, Pandas, Matplotlib, Folium, Plot.ly and SciKit-Learn).

CloudQuant Thoughts… I love examples of real world applications of Machine Learning, often we get stuck in our own very closed off world of data (though I prefer them to detail every step including exactly how they did their web scrape for data!!).

Scientists have created a murder-obsessed ‘psychopath’ AI called Norman — and it learned everything it knows from Reddit

Some people fear Artificial Intelligence, maybe because they’ve seen too many films like “Terminator” and “I, Robot” where machines rise against humanity, or perhaps because they spend too much time thinking about Roko’s Basilisk. As it turns out, it is possible to create an AI that is obsessed with murder.

That’s what scientists Pinar Yanardag, Manuel Cebrian, and Iyad Rahwan did at the Massachusetts Institute of Technology when they programmed an AI algorithm by only exposing it to gruesome and violent content on Reddit, then called it “Norman.” Norman was named after the character of Norman Bates from “Psycho,” and “represents a case study on the dangers of Artificial Intelligence gone wrong when biased data is used in machine learning algorithms,” according to MIT.

Databricks Clears Up AI Dilemma with Unified Analytics

Databricks, founded by the original creators of Apache Spark, has launched new capabilities to lower the barrier for enterprises to innovate with AI. The new capabilities include MLflow for developing an end-to-end machine learning workflow; Databricks Runtime for ML to simplify distributed machine learning; and Databricks Delta for data reliability and performance at scale.
2018-06-05 Read the full story.
2018-06-11 Read the full story.

Programming Best Practices For Data Science

The data science life cycle is generally comprised of the following components:

data retrieval

data cleaning

data exploration and visualization

statistical or predictive modeling

While these components are helpful for understanding the different phases, they don’t help us think about our programming workflow.

Often, the entire data science life cycle ends up as an arbitrary mess of notebook cells in either a Jupyter Notebook or a single messy script. In addition, most data science problems require us to switch between data retrieval, data cleaning, data exploration, data visualization, and statistical / predictive modeling.

But there’s a better way! In this post, I’ll go over the two mindsets most people switch between when doing programming work specifically for data science: the prototype mindset and the production mindset.

In machine learning, we are not always provided an objective to optimize, we are not always provided a target label to classify the input data points into. The kinds of problems where we are not provided with an objective or label to classify is termed as an unsupervised learning problem in the domain of AI. In an unsupervised learning problem, we try to model the latent structured information present within the data. Clustering is a type of unsupervised learning problem where we try to group similar data based on their underlying structure into cohorts/clusters. K-means algorithm is a famous clustering algorithm that is ubiquitously used. K represents the number of clusters we are going to classify our data points into.
2018-06-09 15:45:54.619000+00:00 Read the full story.

Artificial Intelligence in the public and private sectors

You’re not the only one nervous about AI -in light of rapid AI growth and adoption, the U.S. Government recently held three Subcommittee Meetings designed to understand the implications posed by the widespread adoption of AI technology in the public and private sectors. So why is the US Government concerned about AI in society, and what role should it be considering in the private sector? Sid Mair, senior vice president of Federal Systems at Penguin Computing, weighs in.

Beginning his technology career at NASA, Sid brings more than 30 years of expertise across all aspects of the federal market, including the Department of Defense, Homeland Security, civilian agencies in both classified and unclassified areas, as well the Executive and Congressional branches of government. Penguin Computing most recently built the world’s largest AI cluster in the private sector.

Life lessons from artificial intelligence: What Microsoft’s AI chief wants computer science grads to know about the future

Artificial intelligence has exploded, and perhaps no one knows it more than Harry Shum, the executive vice president in charge of Microsoft’s AI and Research Group, which has been at the center of a major technological shift inside the company.

Delivering the commencement address Friday at the University of Washington’s Paul G. Allen School of Computer Science and Engineering, Shum drew inspiration from three emerging technologies — quantum computing, AI, and mixed reality — to deliver life lessons and point out the future of technology for the class of 2018.
2018-06-09 16:57:00-07:00 Read the full story.

Google Building Custom Server Units

As the race to develop customized artificial intelligence (AI) hardware heats up, traditional semiconductor manufacturers could be at risk from new custom chips from some of their largest customers, such as Apple Inc. (AAPL) and Alphabet Inc. (GOOGL). As Mountain View, California-based search giant Alphabet continues to release next-gen versions of its home-grown Tensor Processing Unit (TPU), which first launched in 2016, one team of analysts on the Street suggests that the firm will use the chips to build computers.

The global server market is continuing to expand due in part to demand from large cloud service providers and the rise of modern workloads such as artificial intelligence and analytics. Established server original equipment manufacturers such as Dell and Hewlett Packard Enterprise as well as original design manufacturers are handling much of the demand.

According to IDC, server revenues, shipments and average selling prices all grew in the first quarter of 2018, which was the third consecutive quarter of double-digit revenue growth for the industry and the most revenue generated by the server market in history.

Simple Investing Strategies Stand the Test of Time… or Don’t chase the fads.

Complexity sells. As an ETF issuer, over the past year we have been approached by a dozen “AI machine-learning” creators looking to license their algorithms into an ETF that we would sell. In most of those presentations, when we pressed with even the simplest questions about where the “algo” sources its position signals from or how the mechanics of the security selection work, the responses have illuminated nothing but buzzwords and catchphrases.

We might be passing on the next big thing, and I recognize that is a possibility. Or we could be passing on yet another fad in an industry that has earned a terrible reputation from selling overly complex financial solutions that end up ruining investment accounts and lives. I wouldn’t know which because I’m not qualified to evaluate artificial intelligence. And anyway, I prefer simplicity.

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